Solutions Architect, Higher Education and Research
NVIDIA
Job Overview
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Job Description
Job Overview
NVIDIA is seeking a Solutions Architect, Higher Education and Research. The role focuses on supporting customers in universities and research institutions with high performance computing (HPC) and artificial intelligence solutions. This role is ideal for computational scientists, engineers, and data scientists with a passion for innovative technologies.
Primary Responsibilities
Lead HPC and AI technical engagements, understand customer goals, and deliver innovative solutions using NVIDIA's accelerated computing platforms for ML/DL, data science, and HPC. Collaborate across teams including sales and business development, and be ready for 20% travel.
- Engage customers to understand goals and technical needs.
- Stay updated with AI and scientific computing ecosystems.
- Document and share knowledge via training, whitepapers, and blogs.
- Relay customer requirements to engineering for platform improvements.
- Utilize conferencing tools and travel as required.
Required Qualifications
BS, MS or PhD in Engineering, Mathematics, Physical Sciences, Computer Science or equivalent experience with at least 5 years of relevant work experience. Must have experience in porting or optimizing scientific applications for GPUs, strong programming, and presentation skills.
Preferred Qualifications
Experience with full-stack scientific computing, parallel programming (C/C++, Python, CUDA, OpenACC, OpenMP, MPI), and orchestration/containerization tools like Slurm, Kubernetes, Docker, or Singularity.
Compensation and Application
Salary ranges based on level, with additional equity and benefits. Applications accepted until January 13, 2026. NVIDIA is an equal opportunity employer committed to diversity.
Key skills/competency
- HPC
- AI
- GPU
- Scientific Computing
- Technical Engagement
- Parallel Programming
- Presentation Skills
- Customer Engagement
- Documentation
- Collaboration
How to Get Hired at NVIDIA
- Research NVIDIA's culture: Understand their tech innovations and mission.
- Customize your resume: Highlight HPC and GPU experience.
- Tailor your application: Emphasize academic and research projects.
- Prepare for technical interviews: Practice deep learning and parallel programming questions.
- Network with current employees: Use LinkedIn for insights.
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